provisioning of compression and secure management services ... · by altering medical image and...
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International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064
Index Copernicus Value (2013): 6.14 | Impact Factor (2013): 4.438
Volume 4 Issue 8, August 2015
www.ijsr.net Licensed Under Creative Commons Attribution CC BY
Provisioning of Compression and Secure
Management Services for Healthcare Data in Cloud
Computing
Suhasini Kalki1, Jayashree Agarkhed
2
1PG Student, Computer Science & Engg Department, P.D.A College of Engg, Kalaburagi, India
2Professor, Computer Science & Engg Department, P.D.A College of Engg, Kalaburagi, India
Abstract: Cloud computing is the use of computing resources that are delivered as a service over a network. Cloud computing entrusts
remote services with a user’s data, software and computation. Cloud computing encompasses three main concepts: pay-as-you-go, on-
demand and on the Net. Several studies show that the lack of access to resources and shared data is one of the main causes of errors in
the healthcare sector. Therefore in this work we propose a novel cloud based image compression technique using digital watermarking
which enables embedding one image behind another. We also show that patient’s information can also be stored along with the image
without annotation which helps preserving the quality of the image. We further compare the proposed technique’s performance for both
medical and non medical images and prove that the method is more suitable for medical images due to gray scale nature of the image
data.
Keyword: SaaS cloud, Healthcare, Medical imaging, Lossless compression, Big Data.
1. Introduction
Cloud computing has recently emerged as a new platform
for deploying, managing and provisioning large-scale ser-
vices through an Internet based infrastructure. Cloud compu-
ting refers to both the applications delivered as services over
the Internet, hardware and systems software in the data cen-
ters that provide those services The data centre hardware and
software can be called as ‗cloud‘. When a cloud is made
available in a ‗pay-as-you-go‘ manner to the general public,
it is a ‗public cloud‘ and the service being sold is ‗utility
computing‘. The term ‗private cloud‘ refers to internal data
centers of a business or other organizations which are not
made available to the general public. Cloud computing has
several benefits such as convenience of accessing the data
from anywhere when connected to the internet, security by
encrypting the outsourced data onto the cloud, backups the
data when local computer crashes and collaboration so that
the others can access, view, and modify documents, with
permission. With the cloud, one can access to unlimited sto-
rage capability and scalability and it takes fewer resources to
cloud compute, thus saving energy [1].
The digital images becoming increasingly important in
healthcare environment, since they are more and more used
in medical and medical related applications. Several differ-
ent digital imaging technologies may simultaneously pro-
duce huge amount of data for each patient therefore, their
management may be considered as an typical ―big-data‖
problem. In particular, medical images can be very large in
size they may not be suitable for devices with relatively
limited display capabilities, storage, processing power as
well as slow network access. For this reason the efficient
transmission of such images, mainly in modern network-
centric environments, requires complex network protocols,
along with advanced compression and security techniques.
In particular data compression is essential for efficient
transmission and storage. At the same time, visual quality of
such images is required to be high, in order to ensure correct
and effective analysis resulting in correct diagnoses. In pre-
vious work they have not considered dynamic and adaptive
compression for medical images and security issues regard-
ing to those images in terms of encryption and decryption of
images. In previous work they have considered the ‗static
cloud computing‘ environment, which is not able to evolve
itself in response to the changes in both network topology
and conditions. But here the authors have used the virtual
cloud environment [2].
2. Related Work
Extensive works have been done by many researchers but
existing problem of compression of image along with patient
information and hiding of one image behind another image
as well as security for the images are left open.
This Section mainly focuses on the literature review that has
been carried out for the healthcare management. Several
studies show that the lack of access to resources, that is
some devices does not provides access to medical images
and some provides only storage capacity to the medical im-
ages and other devices are provides processing of medical
images. and shared data is one of the main causes of errors
in the healthcare sector. The problem addressed in [3] is the
inefficient usage of resources as well as the shared data in
the field of healthcare. The medical images are processed
using large number of devices and high network protocols
due to complicated nature of medical images. Implementa-
tions of these medical images are based on advanced com-
pression and security techniques. The authors have proposed
a method for dynamic and adaptive compression of medical
images and also for security used digital invisible water-
marking procedure. The overall architecture uses Software
as a Service (SaaS) in hybrid cloud environment. The result
Paper ID: SUB157665 1603
International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064
Index Copernicus Value (2013): 6.14 | Impact Factor (2013): 4.438
Volume 4 Issue 8, August 2015
www.ijsr.net Licensed Under Creative Commons Attribution CC BY
contains heterogeneous hardware and software capabilities
which are virtualized to the end users in the cloud system.
The 3D medical images are securely managed using virtual
infrastructure-less cloud solution system. The main advan-
tage of this work includes scalability, adaptivity and data
integrity issues. The different security methods endorsed are
public-key cryptography, private-key cryptography and mes-
sage authentication codes. The issue left open is they have
considered only medical images not considered the embed-
ding of patient information along with patient diseased im-
ages, so that the doctor may get confusion with the patient
records. In [4] the author presented a 3D rendering approach
using cloud computing system for the optimization of scala-
bility and operational cost issues for different environments.
The system architecture used is Microsoft Azure cloud plat-
form which comprises of storage service for data storage and
compute service for major computation issues. The 3D algo-
rithm has been implemented using Graphics Processing
Units (GPU) and Central Processing Units (CPU) computa-
tion services. The performance measures of the system is
provided by the size of Virtual Machines for computation
service. The dynamic optimization of system scalability as
well as cost of cloud in distributed environment is consi-
dered in a network infrastructure. In [5] the authors proposed
an data compression approach for 3D images to reduce the
volume of images and their efficient storage along with rela-
tive transmission time through internet or any other ad-hoc
systems like Picture Archiving and Communication System
(PACS), telerediology etc. Since these images are often
stored in system vulnerable from the security point of view,
because they contain sensitive data. The attack can be made
by altering medical image and then may alter the relative
diagnosis. The proposed solution is twofold: the first method
is a low complexity approach is used for compression of 3D
medical images and the second method is to insert invisible
digital watermarks during the compression process. This is
the hybrid approach that handles simultaneously and effi-
ciently both compression and security to 3D images. In [6]
the authors proposed an approach for lossless compression
of continuous tone images, the context modeling is an exten-
sively used parameter for the compression. The new tech-
niques for context modeling of Differential Pulse Code
Modulation (DPCM) errors are introduced that can exploit
context dependent DPCM error structures for the benefit of
compression. To reduce the context dilution, time and space
complexities they have proposed techniques of forming and
quantizing modeling contexts. This results the improved
coding efficiency at low computational cost. In [7] the au-
thors proposed a new low complexity algorithm for hyper
spectral image compression, they have proposed two novel
approaches to lossless coding of Airborne Visible/Infrared
Imaging Spectrometer (AVIRIS) data. One is based on an
inter band, linear predictor and the other is based on an inter
band, least squares optimized predictor. Both algorithms are
coupled to a simple entropy coder. In [8] the authors pro-
posed a single-pass adaptive algorithm that uses context
classification and multiple linear predictors and this is opti-
mized on a pixel-by-pixel basis. The locality is also ex-
ploited in the entropy coding of the prediction error. The
computational complexity can be substantially reduced
without sacrificing the performance by using alternative
methods for the optimization of predictors and also com-
plexity is reduced by replacing the arithmetic coder with
Golomb-Rice codes. In [9] the authors proposed a frame-
work for the implementation of large-scale distributed data
processing scheme for multiple clusters using MapReduce
programming model. A MapReduce framework technique
called as G-Hadoop for large-scale data computing among
multiple clusters are used. The Gfarm file system method is
used and MapReduce task model executes among multiple
clusters in cloud. The G-Hadoop system makes use of paral-
lel processing issues for considering large data sets using
MapReduce technique. The G-Hadoop architecture consists
of master/slave communication model using HDFS file sys-
tem along with Gfarm file system for large datasets. In [10]
the authors proposed a cloud monitoring approach for opti-
mizing the Quality of Service (QoS) for the hosted applica-
tions. It involves dynamically tracking the QoS parameters
to virtualized services such as the physical resources which
they share and applications running on them. The monitor-
ing techniques are used by the cloud provider or application
developer in the following matters, Keeping the cloud ser-
vices and hosted applications operating at peak efficiency.
Detecting variations in service and application performance.
Accounting the SLA violations of certain QoS parameters.
Tracking of leave and join operations of cloud services due
to failures and other dynamic configuration changes.
3. Proposed System
Figure 1: System Architecture of Healthcare management
system
The Fig 1 depicts the overall architecture of proposed sys-
tem, which consists of five different techniques namely
watermarking based compression of the medical images,
downscaling based compression of the images, encryption
and decryption of images using AES technique and embed-
ding of information along with the patient information. First
the image is compressed by using watermarking based com-
pression. Second the patient information is embedded along
with the image by using Embedding of Message module.
Third the image is encrypted by using AES technique.
Fourth the encrypted image is storing on to the cloud.
3.1 Watermarking Based Compression:
Digital watermarking has been proposed as a way to claim
the ownership of the source and owner. Unlike encryption,
watermarking does not restrict access to the data. Once the
Paper ID: SUB157665 1604
International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064
Index Copernicus Value (2013): 6.14 | Impact Factor (2013): 4.438
Volume 4 Issue 8, August 2015
www.ijsr.net Licensed Under Creative Commons Attribution CC BY
encrypted data is decrypted, the intellectual property rights
are no longer protected.
Over the past few years, the technology of the digital wa-
termarking has gained prominence and emerged as a leading
candidate that could solve the fundamental problems of legal
ownership and content authentications for digital multimedia
data. A great deal of research efforts has been focused on
digital image watermarking in recent years. The techniques
proposed so far can be divided into two groups according to
the embedding domain.
Algorithm 1: Watermark Based Compression Technique
(WBCT )
Step 1: The cover image f (i, j) is decomposed into low
pass image and directional sub bands by using
CT decomposition.
Step 2: The low pass image f lo(i, j) lo coefficients are
quantized using the following rule
z = mod( f lo(i, j),Q)
Step 2.1: If w(i, j) = 0 & z < Q/ 2
No modification in f lo (i, j)
Step 2.2: If w(i, j) = 0 & z >= Q/ 2
f lo(i, j) = f lo (i, j)-Q/ 2
Step 2.3: If w(i, j) = 1& z >= Q/ 2
No modification in f lo (i, j)
Step 2.4: If w(i, j) = 1& z < Q/ 2
f lo (i, j) lo = f lo (i, j) +Q/ 2
In Step 1 image f(i, j) is decomposed into low pass image
and by using Contourlet Transform (CT)the directions of
the image are obtained. In Step 2 the low pass image flo(i,j)
is quantized by using the rule. In Step 2.2-2.4 the ‗Q’ is
quantization coefficient and may be selected based the expe-
rimentation on cover image. This is usually a trial and error
process. After modifying flo(i, j), inverse CT is applied
with the modified flo(i, j) and the watermarked image f lo(i,
j)' is obtained.
The image watermarking extraction algorithm is as fol-
lows.
Step 1: The watermarked image f (i, j)' is decomposed into
low pass image and directional sub bands by using CT de-
composition. The watermark image is extracted by using
the following rule.
Step 2: z'= mod( f lo(i, j)',Q)
Step 2.1: If z < Q/ 2 , w(i, j) = 0
Step 2.2: If z >= Q/ 2 , w(i, j) = 1
In step 1 the watermarked image f(i,j)’ is decomposed into
low pass image and by using CT once again obtain the vari-
ous directions of the image. In step 2- 2.2 the watermarked
image is extracted.
3.2 Encryption and decryption of the Images:
The Advanced Encryption Standard (AES), also referenced
as Rijndael (its original name), is a specification for the en-
cryption of electronic data established by the U.S. National
Institute of Standards and Technology (NIST) in 2001. Rijn-
dael is a family of ciphers with different key and block sizes.
For AES, NIST selected three members of the Rijndael
family, each with a block size of 128 bits, but three different
key lengths: 128, 192 and 256 bits. The algorithm described
by AES is a symmetric-key algorithm, meaning the same
key is used for both encrypting and decrypting the data.
Algorithm 2: Advanced Encryption Standard Technique
(AEST)
Step 1: KeyExpansions—round keys are derived from
the cipher key using Rijndael's key schedule. AES
requires a separate 128-bit round key block for each
round plus one more.
Step 2: InitialRound- AddRoundKey—each byte of the
state is combined with a block of the round key us-
ing bitwise xor.
Step 3: Rounds SubBytes—a non-linear substitution
step where each byte is replaced with another ac-
cording to a lookup table.
ShiftRows—a transposition step where the
last three rows of the state are shifted cyclically a
certain number of steps.
MixColumns—a mixing operation which operates
on the columns of the state, combining the four
bytes in each column.
AddRoundKey
Step 4: Final Round (no MixColumns)
SubBytes
ShiftRows
AddRoundKey.
3.3 Message Embedding:
In this concept the information about the patient like, name
of the patient, disease of the patient and other details of the
patient are embedded along with the image by using the
key.
Fig 2: Message Embedding
Algorithm 3: Data Embedding technique using Genetic
Algorithm (DEGA) Step 1: Obtain the size of the authenticating image m x n.
Step 2: For each authenticating message/image,Read
source image block of size 3x3 in row Major order.
Extract authenticating message/image bit one by
one. Replace the authenticating message/image
bit in the rightmost 4 bits within the block, four bit
in each byte.
Paper ID: SUB157665 1605
International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064
Index Copernicus Value (2013): 6.14 | Impact Factor (2013): 4.438
Volume 4 Issue 8, August 2015
www.ijsr.net Licensed Under Creative Commons Attribution CC BY
Step 3: Read one character/ pixel of the authenticating
message/ image at a time.
Step 4: Repeat step 2 and 3 for the whole, authenticating
message/ image size, content.
Step 5: Perform mutation operation for the whole embed-
ded image. For mutation rightmost 3 bits from each
bytes is taken. A consecutive bitwise XOR is per-
formed on it for the 3 steps. It will form a triangular
form and first bit from each step is taken
Step 6: A bit handling method is performed on the embed-
ded image. If the difference between the host and
embedded image is ±16 the 16 will be added to the
embedded image to keep intact the visibility of the
embedded image.
4. Result Analysis
Figure 3: PSNR results by embedding two images.
The PSNR is the ratio between the maximum possible power
of a signal and the power of corrupting noise that affects the
fidelity of its representation. PSNR is usually expressed in
terms of the logarithmic decibel scale. The signal in this case
is the original data and the noise is the error introduced by
compression. PSNR is most easily defined via the Mean
Squared Error (MSE). Given the noise free M×N monoch-
rome image I and its noise approximation K.
MSE = 𝟏
𝐦𝐧∑[𝐈 𝐢, 𝐣 − 𝐊 𝐢, 𝐣 ]2 (1)
The PSNR (in dB) is defined as,
PSNR = 10 log10 (MAX2
I)/MSE (2)
MAXi is the maximum possible pixel value of the image,
when the pixels are represented using 8-bits per sample, this
is 255. When samples are represented using linear PCM with
B bits per sample, MAXi is 2B-1.
PSNR and MSE are used to comparing the squared error
between the original image and the reconstructed image.
PSNR is high means good quality and low means bad quali-
ty. PSNR is using a term MSE in the denominator, so low
the error, high will be the PSNR.
PSNR depends on MSE between original and processed
image. Theoretically, PSNR can be infinite if MSE is zero.
MSE is zero when there is no difference between original
and processed image.
Figure 4: Compression performance of medical images.
The Fig 4 shows the compression ratio for the medical im-
ages, this is defined as,
Compression Ratio = Uncompressed / Compressed
(3)
The space saving can be calculated by,
Space Saving=1- Uncompressed Size/ Compressed Size
(4)
5. Conclusion
The sharing of medical information resources is a key factor
playing a fundamental role towards the effective and rapid
medical diagnosis, especially when doctors are faced with
new clinical cases or whether they should take into account
diseases that are not properly part of their domain of compe-
tence or their own specialization. In this work we presented
a Virtual infrastructure-less Cloud solution for secure man-
agement of 3D medical images, which operates in an almost
completely transparent manner, regardless of computational
and networking capabilities which users can avail in any
given moment.
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International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064
Index Copernicus Value (2013): 6.14 | Impact Factor (2013): 4.438
Volume 4 Issue 8, August 2015
www.ijsr.net Licensed Under Creative Commons Attribution CC BY
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